Regresion_Lineal.ipynb

regresion_lineal_grillos.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "R",
      "language": "R",
      "name": "ir"
    },
    "language_info": {
      "codemirror_mode": "r",
      "file_extension": ".r",
      "mimetype": "text/x-r-source",
      "name": "R",
      "pygments_lexer": "r",
      "version": "3.6.3"
    },
    "colab": {
      "name": "Regresion_Lineal.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/rafastaria/070cafb9180fdd778fc0c3921c624a51/regresion_lineal.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p91Lx7FM39i6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#lectura de datos\n",
        "dat <- read.csv(\"grillos.csv\")\n",
        "\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xG5_TGZ14d9u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "796597ba-7b14-42c5-c2de-aa7292cfd55d"
      },
      "source": [
        "head(dat)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  chirridos temp\n",
              "1 20.0      88.6\n",
              "2 16.0      71.6\n",
              "3 19.8      93.3\n",
              "4 18.4      84.3\n",
              "5 17.1      80.6\n",
              "6 15.5      75.2"
            ],
            "text/latex": "A data.frame: 6 × 2\n\\begin{tabular}{r|ll}\n  & chirridos & temp\\\\\n  & <dbl> & <dbl>\\\\\n\\hline\n\t1 & 20.0 & 88.6\\\\\n\t2 & 16.0 & 71.6\\\\\n\t3 & 19.8 & 93.3\\\\\n\t4 & 18.4 & 84.3\\\\\n\t5 & 17.1 & 80.6\\\\\n\t6 & 15.5 & 75.2\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 6 × 2\n\n| <!--/--> | chirridos &lt;dbl&gt; | temp &lt;dbl&gt; |\n|---|---|---|\n| 1 | 20.0 | 88.6 |\n| 2 | 16.0 | 71.6 |\n| 3 | 19.8 | 93.3 |\n| 4 | 18.4 | 84.3 |\n| 5 | 17.1 | 80.6 |\n| 6 | 15.5 | 75.2 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 6 × 2</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>chirridos</th><th scope=col>temp</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>20.0</td><td>88.6</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>16.0</td><td>71.6</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>19.8</td><td>93.3</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>18.4</td><td>84.3</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>17.1</td><td>80.6</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>15.5</td><td>75.2</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JnLTGSD65cgp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mean(dat$chirridos)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NOwZEvM15hR6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "min(dat$chirridos)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OH5uO1aW5j5P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "max(dat$chirridos)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsGdjNo75pbR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "7bfb8dd4-ffaa-459b-aea5-57225f080a54"
      },
      "source": [
        "summary(dat$chirridos)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
              "  14.40   15.45   16.20   16.65   17.15   20.00 "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IYBvpG4g6WDs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c1ce9c0c-015c-4b71-b5b6-d3ce269ff095"
      },
      "source": [
        "summary(dat$temp)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
              "  69.40   75.75   80.60   80.04   83.40   93.30 "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKxegmNZ6ra-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hist(dat$chirridos,xlab=\"Chirridos\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dN1ux_h67Fl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hist(dat$temp)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_cDqmM37F_C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "boxplot(dat$chirridos)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zf4ibW0R7Lv6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "5c265a94-c185-4467-d18e-59fb2690d6b9"
      },
      "source": [
        "boxplot(dat$temp)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aRGZecXUl9lF6lR2fmF1FfZRWpUdn4h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\nkCCAkCCAkCCAkCCAkCCAkCCAkCDA/wJog01Nj3a0VwAAAABJRU5ErkJggg==",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7lEUhrF74FV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "dfd5170c-ba17-4659-f21c-5b6ee7e80576"
      },
      "source": [
        "#Gráfica de dispersión\n",
        "#Scatter plot\n",
        "#dat$temp = característica y dat$chirridos es la etiqueta\n",
        "\n",
        "plot(dat$temp,dat$chirridos, xlab =\"Temp\", ylab=\"Chirridos\")"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zzlja9sD4EEIiQvvN40/bTG5kp+plPyIiQPTEt/cJ/jLO9y1n7bkyBRCCnxCluN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\nAElFTkSuQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T16PyFEo8cxY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fb5fd27e-af03-461d-f056-21502fffbb11"
      },
      "source": [
        "#Correlación entre dos variables\n",
        "#Si es muy cercano a uno, hay mucha correlación.\n",
        "#Si es cercano a cero, las variables son independientes.\n",
        "#Si la correlación es negativa, hay una relación linal pero cuando una crece, la otra decrece.\n",
        "cor(dat$temp, dat$chirridos)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 0.8351438"
            ],
            "text/latex": "0.835143787031155",
            "text/markdown": "0.835143787031155",
            "text/html": [
              "0.835143787031155"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j4IasAkO9L1l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "00846f1c-6d79-4ae3-cdaf-a6d4f478e40a"
      },
      "source": [
        "#Linear model\n",
        "r <- lm(chirridos ~ temp, data = dat)\n",
        "\n",
        "#Resumen estadístico\n",
        "summary(r)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = chirridos ~ temp, data = dat)\n",
              "\n",
              "Residuals:\n",
              "     Min       1Q   Median       3Q      Max \n",
              "-1.56009 -0.57930  0.03129  0.59020  1.53259 \n",
              "\n",
              "Coefficients:\n",
              "            Estimate Std. Error t value Pr(>|t|)    \n",
              "(Intercept) -0.30914    3.10858  -0.099 0.922300    \n",
              "temp         0.21192    0.03871   5.475 0.000107 ***\n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 0.9715 on 13 degrees of freedom\n",
              "Multiple R-squared:  0.6975,\tAdjusted R-squared:  0.6742 \n",
              "F-statistic: 29.97 on 1 and 13 DF,  p-value: 0.0001067\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dO6DJPhD-lb3",
        "colab_type": "text"
      },
      "source": [
        "# *chirridos* = 0.31 + 0.21 *temp*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hf4SiHD1-65g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "12301f5e-cada-423e-d7ab-88d5407a1b63"
      },
      "source": [
        "#Gráfica de dispersión con línea de modelo lineal\n",
        "plot(dat$temp,dat$chirridos)\n",
        "abline(r,col=\"red\")"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C1erE8+0dChRqdxPhQ7AADgGI4dU58+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            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tc4skFYhBmpC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "6db8848c-acc2-485f-9b0f-828274edfef8"
      },
      "source": [
        "#Usando el modelo lineal, para 84 F, se determina el número de chirridos\n",
        "print(\"La temperatura es\")\n",
        "-0.31+0.21*84\n"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1] \"La temperatura es\"\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 17.33"
            ],
            "text/latex": "17.33",
            "text/markdown": "17.33",
            "text/html": [
              "17.33"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}